Abstract
Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. In the present report, we addressed these limitations by developing Programmable Enrichment via RNA FlowFISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations defined by the abundance of specific RNA transcripts. Across immune populations (n = 184,126 cells) and fresh-frozen and formalin-fixed, paraffin-embedded brain tissue (n = 33,145 nuclei), we demonstrated that programmable sorting logic via RNA-based cytometry can isolate rare cell populations and uncover phenotypic heterogeneity via downstream, high-throughput, single-cell genomics analyses.
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Data availability
Sequencing data associated with this work are available on the Gene Expression Omnibus (GEO) at accession no. GSE262355. A full step-by-step protocol for PERFF-seq is available on protocols.io (https://doi.org/10.17504/protocols.io.14egn3k6ql5d/v1 and https://doi.org/10.17504/protocols.io.8epv5x8r4g1b/v1).
Code availability
Customized code and intermediate code files to reproduce all analyses supporting this manuscript are available at https://github.com/clareaulab/perffseq_reproducibility and via Zenodo at https://doi.org/10.5281/zenodo.14089656 (ref. 52).
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Acknowledgements
We thank the Satpathy Lab, Lareau Lab, Gladstone Flow Cytometry Core and Single-cell Analytics Innovation Lab members for helpful discussions. We acknowledge a helpful blog post from 10x Genomics describing the singlet unligated probe set. We thank T. Nawy for helpful feedback on the manuscript, A. Chow for assistance with flow cytometry and N. Pereira with S. Jovanovich of S2 Genomics for support with nuclei isolation from FFPE tissue. This work was supported by National Insitutes of Health grants (nos. P30CA008748, R00HG012579 and U01AT012984 to C.A.L. and UM1HG012076 to A.T.S. and L.S.L.) and a Gates Foundation seed award. A.T.S. is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists, the Parker Institute for Cancer Immunotherapy, a Pew-Stewart Scholars for Cancer Research Award, a Cancer Research Institute Lloyd J. Old STAR Award, a Scholar Award from the American Society of Hematology and a Baxter Foundation Faculty Scholar Award. Y.H.H. is supported by a PhD fellowship from the Hector Fellow Academy. L.S.L. is supported by the Hector Fellow Academy, the Paul Ehrlich Foundation, the European Molecular Biology Organization Young Investigator Programme, an Emmy Noether fellowship (grant no. LU 2336/2-1) and grants by the German Research Foundation (Dnos. LU 2336/3-1, LU 2336/6-1, STA 1586/5-1, TRR241 and SFB1588, and Heinz Maier-Leibnitz Award). The Single-cell Analytics Innovation Lab is supported by the Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center at Memorial Sloan Kettering. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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T.A., R.R.S., M.T.T., R.C., A.T.S. and C.A.L. conceived and designed this work. T.A. and R.R.S. led assay development with input from A.T.S. and C.A.L. T.A., R.R.S., M.T.T., B.N.N., Y.-H.H., S.H. and C.S. performed experiments. K.K.H.Y., Z.A.-M., V.T., B.E.H. and L.S.L. aided in the interpretation of data analyses. R.R.S., R.C., A.T.S. and C.A.L. supervised the work. C.A.L. led bioinformatics analyses with input from T.A. and R.R.S. T.A. led the development of the protocol with input from R.R.S. and M.T.T. T.A., R.R.S., A.T.S. and C.A.L. drafted the manuscript with input from the other authors.
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A.T.S. is a founder of Immunai, Cartography Biosciences, Santa Ana Bio and Prox Biosciences, an advisor to Wing Venture Capital and receives research funding from Astellas and Merck Research Laboratories. R.R.S., L.S.L. and C.A.L. are consultants to Cartography Biosciences. R.C. is a consultant for Sanavia Oncology, S2 Genomics and LevitasBio. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Analyses supporting PERFF-seq development.
(a) Schematic overview of Flex workflow, including probe hybridization to transcript fragments in cells upstream of Chromium and bead oligo extension of the ligation product. (b) Representative Bioanalyzer (Agilent Technologies) trace outlining complete versus incomplete sequencing molecules. (c) Graphical summary of probe capture sequence (pCS1) bead oligo capture sequence (left) and percent of reads with pCS1 detected in first 25 bases. (d) Comparison of FlowFISH signal using either unstained cells or the hairpin only comparing the sorted CD3E+ population and/or stripped via formamide. (e) Same as in (d) but using dsDNase for stripping. Note: quantifying fluorescence after sorting/stripping (d, purple; e, blue) is not standard for the PERFF-seq protocol but shown here as part of assay development. (f) Bioanalyzer traces for representative libraries from panels in Fig. 1, highlighting half- and fully-mapped probes. (g) Bioanalyzer traces of library preparation where the full PERFF-seq workflow was completed except omiting the dsDNAse stripping step.
Extended Data Fig. 2 Supporting analyses of assay benchmarking.
(a) HCR FISH staining and quantification of EPCAM across cell lines. Mean fluorescence intensity (MFI) and bulk RNA-seq transcripts per million (TPM from49) are noted for each condition. (b) Replication of cell line mixing experiment and staining for XIST. (c) Reduced dimensionality embedding of all cells in the four-plex benchmarking experiment. PERFF-seq was enriched for CD3E+ cells. No probe, no sort (NPNS) and Yes probe, no sort (YPNS) profile all PBMC subpopulations with minor modifications to the reaction. (d) Marker genes supporting annotation of key populations. (e) Same as (c) but stratified by library. Boxes indicate B cell and monocyte populations that are depleted from the PERFF-seq library. (f) Percent of T cells from each library with at least 1 UMI for CD3D or CD3E. (g) log2 counts per million (CPM) of CD3D and CD3E across different library conditions. (h) Differentially expressed genes between different facets of PERFF-seq compared to Flex. Two genes were differentially expressed, including CD3E in both the YPNS and PERFF-seq conditions. ∅ means empty or no genes detected.
Extended Data Fig. 3 Supporting analyses of combinatorial PBMC cell states.
(a) Additional marker genes for distinct populations from PBMC cell type analyses. Arrows indicate markers for rare populations expected from PBMC profiling. (b) Relative enrichment of cell populations (colours) for each PERFF-seq library compared to the Flex PBMC library. (c) Subclustering of CD3E+/CD4+ cells, highlighting rare subclusters marked by relevant genes. (d) Summary of CD4 HCR FISH signal, stratified by CD3E populations. (e) Bulk RNA-seq expression of CD4 from FACS-isolated populations21. Replicates for e are all libraries from Haemopedia21 with no statistical test. (f) Design and results of antibody and HCR FISH co-staining to evaluate CD4 RNA and protein expression.
Extended Data Fig. 4 Supporting analyses of unconventional enrichments of rare cell states.
(a) Azimuth Violin plots for BCL11A and SPI1 RNA expression across well-annotated populations in peripheral blood mononuclear cell types. (b) Summary of FACS populations, including unsorted, BCL11A+, and SPI1+ populations. (c) Additional marker genes supporting cell type annotations. (d) Empirical cumulative distribution plot of scaled expression of BCL11A (left) and SPI1 (right) stratified by the captured PERFF-seq library. (e) Bulk RNA-seq of sorted populations of BCL11A21. (f) Design (left) and results (right) of cytometry analysis of PBMCs co-stained with BCL11A mRNA (via HCR-FISH) and CD19 and CD123 protein (via antibodies). Mean fluorescence intensity (MFI) for BCL11A of each population is quantified. (g) Comparison of B cells from BCL11A+ FlowFISH or negative/SPI1+ populations for BCL11A expression or BCL11A target gene module scores. Uncorrected p-value for the two-sided Wilcoxon rank-sum test is shown. (h) Additional violin plots of marker genes, stratified by the FlowFISH library. All genes were significantly differentially expressed at a false discovery rate (FDR) < 0.01. (i) Summary of IL3RA+ FACS sort and population characterized with PERFF-seq. (j) Proportion of cell types from the Azimuth L1 reference for IL3RA+/- PERFF-seq libraries. (k) Reduced dimensionality representation of IL3RA+ PERFF-seq library, highlighting profiled AS DCs. (l) Gene-gene Spearman correlations of all AS DCs from the IL3RA+ sort. Genes match those in Fig. 4k. (m) Summary of CD123 expression from antibody-derived tags (ADT) of PBMC CITE-seq. (n) Bulk RNA-seq of sorted populations of IL3RA21. Replicates for e and n are all libraries from Haemopedia21 with no statistical test.
Extended Data Fig. 5 Supporting analyses for mosaic loss of Y chromosome.
(a) FlowFISH cytometry gating scheme, including control (no probe, left) and four male (XY) donors of different ages. The percent of cells corresponding to MSY+ (red) and MSY- (blue) in each donor gate are shown as numeric values. (b) Empirical cumulative distribution plot of the percent of Y chromosome UMIs stratified by the captured PERFF-seq library. Percentages of cells with 0 MSY UMIs from the scRNA-seq library are noted. P-values are from a two-sided Kolmogorov–Smirnov test comparing the distributions of the positive and negative samples.
Extended Data Fig. 6 Supporting analyses of nuclei enrichment from fresh and fixed tissues.
(a) UMAP of adult mouse cerebellum atlas26, including cell types (top) and Mobp expression (bottom). The Mobp+ oligodendrocytes and oligodendrocyte precursors are circled with their frequency noted. (b) Reduced dimensionality representation of public GBM FFPE Flex data showing 17 clusters. (c) Annotation of marker genes for cluster 10, the population highlighted by the arrow in (b). (d) Supporting marker genes annotating other subpopulations from the PERFF-seq experiment, including the primary cluster of granule cells. (e) Additional marker genes from Mobp+ cells were profiled with PERFF-seq. Atp-associated genes (Atp1b1, Aqp4) supporting rare subclsuters are highlighted in the boxes as well as marker genes highly expressed in all cells. (f) Empirical cumulative distribution plot of the raw UMI counts for each of the three genes enriched via FlowFISH, stratified by the captured PERFF-seq library. (g) Additional marker genes showing heterogeneity defining subclusters of endothelial cells and pericytes.
Supplementary information
Supplementary Information
Supplementary Note on the design of PERFF-seq experiments.
Supplementary Tables
Supplementary Tables 1–6.
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Abay, T., Stickels, R.R., Takizawa, M.T. et al. Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing. Nat Genet 57, 451–460 (2025). https://doi.org/10.1038/s41588-024-02036-7
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DOI: https://doi.org/10.1038/s41588-024-02036-7
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